Weave
engineering intelligence, developer productivity, AI ROI, token intelligence, code quality, prompt routing, engineering analytics, SDLC optimization
About
Weave is an engineering and token intelligence platform that helps organizations understand and improve software development in the AI era. It brings engineering activity, code quality, AI adoption, and model spending into one view, giving leaders and developers a clearer picture of how work moves from prompt to production. Teams can use Weave to connect their investment in AI tools with the outcomes that matter: higher output, better software, and more efficient delivery.
At the core of Weave is a combination of large language models and domain-specific machine learning designed to understand engineering work. The platform analyzes the impact of code changes, how AI contributed, and where friction slows progress. It combines AI metrics with established frameworks such as DORA and SPACE, alongside surveys and other engineering signals. This helps teams evaluate performance with the context needed to understand what their activity actually produces.
Weave’s engineering intelligence gives teams visibility into output, quality, reviews, and engineering health. Leaders can identify bottlenecks, understand differences across teams, and benchmark performance against other engineering organizations. These insights help answer practical questions: Where is delivery getting stuck? Which teams are seeing improvements from AI? Where could a change in tools or working practices make the biggest difference? Teams can use those answers to focus improvement efforts and track the results.
Token intelligence extends that visibility to AI consumption and cost. Weave helps organizations understand which models and tools they use, where tokens are going, and how spending relates to engineering outcomes. By examining cost, efficiency, and quality together, teams can assess whether greater AI usage is producing greater value. Benchmarking adds context, helping organizations understand how their return on token spend compares with others and where there may be opportunities to improve.
Weave also provides tools to act on those findings. Its prompt router classifies requests and directs them to models based on co
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
If you've done that review mining and gone through all those reviews and kept a nice log of the reviews — yes you can speed it all up with AI but I really believe strongly in also just spending a few hours just completely immersing yourself in the language. You'll have this extra benefit on top of that with the messaging for the copy side of it where suddenly your language becomes more human.
Read reviews end-to-end yourself — AI summaries strip the linguistic osmosis
AI can tag and cluster reviews 10x faster, but skipping the manual read costs you the unconscious absorption of customer phrasing. Future ad copy, landing pages, and push notifications will sound like a marketer wrote them instead of a user. Spend a few hours actually reading reviews end-to-end before letting AI summarize.
if you just run one campaign with one adset in it all that data will be used to basically fine tune targeting for you so you always want to find the right balance of where do I need granularity to guide the algorithm
Start with a maximally consolidated Meta account — one campaign, one ad set, all signal in one place
Meta's algorithm needs enough signal data to optimize effectively — scattering budget across 10 campaigns and 50 ad sets starves each one. Burke's default for new accounts is maximum consolidation: one campaign, one broad ad set, all signal funneled to the same place. Add campaign branches only as spend scales and after confirming what you're splitting on.
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